library(pagoda2)
Loading required package: Matrix
Loading required package: igraph
package ‘igraph’ was built under R version 3.5.3
Attaching package: ‘igraph’
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
union
library(conos)
Attaching package: ‘conos’
The following objects are masked _by_ ‘.GlobalEnv’:
plotClusterBarplots, plotComponentVariance, plotDEheatmap
library(Matrix)
library(parallel)
library(cowplot)
Loading required package: ggplot2
Attaching package: ‘cowplot’
The following object is masked from ‘package:ggplot2’:
ggsave
Load original conos object and annotations
scon <- Conos$new(readRDS("~pkharchenko/m/scadden/bmmet/jan2019/scon.rds"))
load("~pkharchenko/m/scadden/bmmet/jan2019/annotations.RData")
Look at the coarse annotation:
alpha <- 0.1; size <- 0.2;
p1 <- scon$plotGraph(groups=typefc,palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F)
p1

Look at the detailed annotation:
alpha <- 0.1; size <- 0.2;
p2 <- scon$plotGraph(groups=typef,alpha=alpha,size=size,mark.groups=T,plot.na=F)
p2


Doublets
source("~/m/pavan/DLI/conp2.r")
data.table 1.11.8 Latest news: r-datatable.com

alpha <- 0.1; size <- 0.2;
p1 <- scon$plotGraph(groups=typefc[doublet.f[names(typefc)]<=0.25],palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F)
p1

Marker genes
Marker genes
nfac <- as.factor(typefc[doublet.f[names(typefc)]<=0.25])
annot.de <- scon$getDifferentialGenes(groups=nfac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
#source("~/m/p2/conos/R/plot.R")
pp <- plotDEheatmap(scon,nfac,annot.de,n.genes.per.cluster = 20 ,show.gene.clusters=T,column.metadata=list(samples=scon$getDatasetPerCell()),order.clusters = T, column.metadata.colors = list(clusters=typefc.pal),use_raster = T,raster_device = "CairoPNG")
pdf(file='annot.heatmap.pdf',width=10,height=30); print(pp); dev.off();
null device
1
A small version of the hetmap, for the main figure
source("~/m/p2/conos/R/plot.R")
genes <- c('MS4A1','CD79A','CD79B','MZB1','VPREB3','SEC11C','IGLL5','GNLY','GZMB','LILRA4','AZU1','MPO','FCN1','IER3','C5AR1','IGJ','SOX4','STMN1','MYL9','HBD','GZMK','AR','KLK2')
pp <- plotDEheatmap(scon,nfac,annot.de,n.genes.per.cluster = 20 ,show.gene.clusters=T, column.metadata.colors = list(clusters=typefc.pal), order.clusters = T, additional.genes = genes, labeled.gene.subset = genes, min.auc = 0.6,use_raster = T,raster_device = "CairoPNG")
pp

alpha <- 0.5; size <- 0.1;
scon$plotGraph(gene='KRT3',alpha=alpha,size=size,mark.groups=F,plot.na=F)
Error in scon$plotGraph(gene = "KRT3", alpha = alpha, size = size, mark.groups = F, :
gene KRT3 is not found in any of the samples
Sample panel:
pp <- scon$plotPanel(groups=nfac,plot.na=F,alpha=0.3,size=0.5,use.common.embedding = F, ncol=4, mark.groups=T,palette=typefc.pal,raster=T, raster.width=3,raster.height=3,title.size=4,font.size=c(3,4))
#pdf('panel.pdf',height=8,width=2); print(pp); dev.off();
pp

Just the Tumor fractions
pl <- lapply(grep("Tumor",names(scon$samples),val=T),function(nam) {
sccore::embeddingPlot(ncon$samples[[nam]]$embeddings$PCA$tSNE,groups=nfac,palette=typefc.pal,plot.na=F,raster=T,raster.height=3,raster.width=3,font.size=c(3,4)) + ggtitle(nam) +theme_bw() + theme(legend.position = "none") + theme(axis.ticks = element_blank(), axis.title = element_blank(), axis.text = element_blank(),panel.grid.major = element_blank(), panel.grid.minor=element_blank())
})
plot_grid(plotlist=pl,nrow=3)

pl <- lapply(grep("Tumor",names(scon$samples),val=T),function(nam) {
sccore::embeddingPlot(ncon$samples[[nam]]$embeddings$PCA$tSNE,colors=conos:::getGeneExpression(ncon$samples[[nam]],'KLK2'),plot.na=F,raster=T,raster.height=3,raster.width=3,font.size=c(3,4)) + ggtitle(nam) +theme_bw() + theme(legend.position = "none") + theme(axis.ticks = element_blank(), axis.title = element_blank(), axis.text = element_blank(),panel.grid.major = element_blank(), panel.grid.minor=element_blank())
})
no non-missing arguments to min; returning Infno non-missing arguments to max; returning -Inf
plot_grid(plotlist=pl,nrow=3)

Expression difference magnitude analysis
An extended version, non-paired comparisons.
source("~pkharchenko/pavan/DLI/conp2.r")
cannot open file '/home/pkharchenko/pavan/DLI/conp2.r': No such file or directoryError in file(filename, "r", encoding = encoding) :
cannot open the connection
JS-based distances
rlist <- list("Tumor vs. Benign"=xTB,"Involved vs. Benign"=xIB,"Tumor vs. Involved"=xIT,"Tumor vs. Distal"=xTD,"Involved vs. Distal"=xID)
pl <- lapply(names(rlist),function(nam) {
ggplot(rlist[[nam]]$df,aes(x=as.factor(Type),y=value)) +
geom_boxplot(notch=T,outlier.shape=NA) +
geom_jitter(position=position_jitter(0.1), aes(color=patient),show.legend=FALSE,alpha=0.1) +
geom_hline(yintercept = 1,linetype=2,color='gray80') +
ylim(0,min(5,max(rlist[[nam]]$df$value)))+
theme(axis.text.x=element_text(angle = 90, hjust=1), axis.text.y=element_text(angle=90, hjust=0.5)) +
ggtitle(nam)+
labs(x="", y="normalized distance")
})
plot_grid(plotlist=pl,nrow=2)
notch went outside hinges. Try setting notch=FALSE.
notch went outside hinges. Try setting notch=FALSE.
notch went outside hinges. Try setting notch=FALSE.
Removed 2 rows containing missing values (geom_point).notch went outside hinges. Try setting notch=FALSE.
Removed 2 rows containing missing values (geom_point).Removed 8 rows containing non-finite values (stat_boxplot).notch went outside hinges. Try setting notch=FALSE.
notch went outside hinges. Try setting notch=FALSE.
Removed 8 rows containing missing values (geom_point).Removed 10 rows containing non-finite values (stat_boxplot).notch went outside hinges. Try setting notch=FALSE.
notch went outside hinges. Try setting notch=FALSE.
Removed 10 rows containing missing values (geom_point).

Correlation-based figure
rlist <- list("Tumor vs. Benign"=xTBc,"Involved vs. Benign"=xIBc,"Tumor vs. Distal"=xTDc,"Involved vs. Distal"=xIDc)
pl <- lapply(names(rlist),function(nam) {
ggplot(rlist[[nam]]$df,aes(x=as.factor(Type),y=value)) +
geom_boxplot(notch=T,outlier.shape=NA) +
geom_jitter(position=position_jitter(0.1), aes(color=patient),show.legend=FALSE,alpha=0.1) +
geom_hline(yintercept = 1,linetype=2,color='gray80') +
#ylim(0,min(5,max(rlist[[nam]]$df$value)))+
ylim(0,5)+
theme(axis.text.x=element_text(angle = 90, hjust=1), axis.text.y=element_text(angle=90, hjust=0.5)) +
ggtitle(nam)+
labs(x="", y="standardized distance")
})
pp <- plot_grid(plotlist=pl,nrow=2)
Removed 6 rows containing non-finite values (stat_boxplot).notch went outside hinges. Try setting notch=FALSE.
notch went outside hinges. Try setting notch=FALSE.
Removed 6 rows containing missing values (geom_point).notch went outside hinges. Try setting notch=FALSE.
notch went outside hinges. Try setting notch=FALSE.
Removed 26 rows containing non-finite values (stat_boxplot).notch went outside hinges. Try setting notch=FALSE.
notch went outside hinges. Try setting notch=FALSE.
notch went outside hinges. Try setting notch=FALSE.
Removed 26 rows containing missing values (geom_point).Removed 10 rows containing non-finite values (stat_boxplot).notch went outside hinges. Try setting notch=FALSE.
notch went outside hinges. Try setting notch=FALSE.
Removed 10 rows containing missing values (geom_point).
pdf(file='difference.magnitudes.pdf',width=7,height=8); print(pp); dev.off();
png
2
pp

Realignment of the Tumor fraction without translation/mitochondrial genes
p2l <- lapply(lapply(dl,function(x) x[,!doublet.f[colnames(x)]>0.25]), basicP2proc, n.cores=30, min.cells.per.gene=0,min.transcripts.per.cell=1e3, n.odgenes=2e3, get.largevis=FALSE, make.geneknn=FALSE)
Remove translation and respiratory chain genes that are bringing tumor cells close to progentiors
library(biomaRt)
ensembl = useMart("ensembl",dataset="hsapiens_gene_ensembl")
#gene.data <- getBM(attributes=c('hgnc_symbol'),filters = 'go', values = "GO:0000280", mart = ensembl)
#getBM(attributes = c('entrezgene_id','hgnc_symbol'), filters = 'go', values = 'GO:0000278', mart = ensembl)
#getBM(attributes = c('entrezgene_id','hgnc_symbol'), filters = 'go', values = 'GO:0006260', mart = ensembl)
tr.genes <- getBM(attributes = c('entrezgene_id','hgnc_symbol'), filters = 'go', values = c('GO:0006412','GO:0006414','GO:0022904','GO:0055114','GO:0070125','GO:0006839'), mart = ensembl)
tr.genes <- tr.genes$hgnc_symbol
saveRDS(tr.genes,file='tr.genes.rds')
tr.genes <- readRDS("tr.genes.rds")
p2l <- lapply(lapply(dl,function(x) x[!rownames(x) %in% tr.genes,!doublet.f[colnames(x)]>0.25]), basicP2proc, n.cores=30, min.cells.per.gene=0,min.transcripts.per.cell=1e3, n.odgenes=2e3, get.largevis=FALSE, make.geneknn=FALSE)
Conos integration of only tumor fractions:
ncon <- Conos$new(p2l[grepl("Tumor",names(p2l))],n.cores=30)
#ncon$buildGraph(k=15, k.self=5, space='CPCA', ncomps=30, n.odgenes=2000, verbose=TRUE)
#ncon$buildGraph(k=15, k.self=5, space='CCA', ncomps=30, n.odgenes=2000, verbose=TRUE)
ncon$buildGraph(k=15, k.self=5, space='PCA', ncomps=30, n.odgenes=2000, verbose=TRUE)
ncon$embedGraph(method='UMAP');
# store different embeddings
ncon$misc$embeddings <- list()
ncon$misc$embeddings$umap <- ncon$embedding
ncon$embedGraph(sgd_batches=2e8,apha=1.5);
ncon$misc$embeddings$lv <- ncon$embedding
ncon$findCommunities(method=leiden.community,resolution=1)
Take a look
alpha <- 0.1; size <- 0.5;
ncon$embedding <- ncon$misc$embeddings$umap
n1 <- ncon$plotGraph(groups=typefc,palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F)
n2 <- ncon$plotGraph(groups=typef,alpha=alpha,size=size,mark.groups=T,plot.na=F,font.size=c(3,5))
#n3 <- ncon$plotGraph(groups=ncon$getDatasetPerCell(),alpha=alpha,size=size,mark.groups=F,plot.na=F)
n3 <- ncon$plotGraph(alpha=alpha,size=size,mark.groups=T,plot.na=F)
plot_grid(plotlist=list(p1,n1,n2,n3),nrow=2)

alpha <- 0.1; size <- 0.3;
n1 <- ncon$plotGraph(groups=typefc,palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F,font.size=c(3,4),raster=T,raster.width=3,raster.height=3)
pdf(file='tumor.nometab.embedding.pdf',width=3,height=3); print(n1); dev.off();
null device
1
n1

alpha <- 0.2; size <- 0.1;
#n1 <- ncon$plotGraph(groups=typefc,palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F)
n2 <- ncon$plotGraph(groups=typef,alpha=alpha,size=size,mark.groups=T,plot.na=F,font.size=c(3,4))
n3 <- ncon$plotGraph(gene='KLK2',alpha=alpha,size=size,mark.groups=F,plot.na=F)
plot_grid(plotlist=list(n2,n3),nrow=1)

Contrast tumor cluster against progenitor cluster 38
x <- ncon$clusters[[1]]$groups
tumor.cells <- names(x)[x==42]
prog.cells <- names(x)[x==38]
unfac <- typefc[doublet.f[names(typefc)]<=0.25]
unfac[names(unfac)%in% tumor.cells] <- 'Tumor'
unfac <- as.factor(unfac)
tfac <- ncon$getDatasetPerCell()
tfac <- tfac[!names(tfac) %in% prog.cells]
tfac <- as.factor(setNames(ifelse(names(tfac) %in% tumor.cells,'tumor','other'),names(tfac)))
tumor.de <- ncon$getDifferentialGenes(groups=tfac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
pfac <- ncon$getDatasetPerCell()
pfac <- pfac[!names(pfac) %in% tumor.cells]
pfac <- as.factor(setNames(ifelse(names(pfac) %in% prog.cells,'prog','other'),names(pfac)))
prog.de <- ncon$getDifferentialGenes(groups=pfac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
cfac <- ncon$getDatasetPerCell()
cfac <- as.factor(setNames(ifelse(names(cfac) %in% c(prog.cells,tumor.cells),'comb','other'),names(cfac)))
comb.de <- ncon$getDifferentialGenes(groups=cfac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
# try with the entire progenitor population, balancing sizes
cfac <- ncon$getDatasetPerCell()
x <- names(typefc)[typefc=="Progenitors"];
xs <- sample(x,length(tumor.cells))
pfac <- pfac[!names(pfac) %in% setdiff(x,xs)]
cfac <- as.factor(setNames(ifelse(names(cfac) %in% c(xs,tumor.cells),'comb','other'),names(cfac)))
comb.de2 <- ncon$getDifferentialGenes(groups=cfac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
Top tumor markers
require(dplyr)
tde <- arrange(tumor.de$tumor,by=desc(AUC))
tde
pde <- arrange(prog.de$prog,by=desc(AUC))
pde
arrange(comb.de2$comb,by=desc(AUC))
write(head(comb.de2,100)$Gene,file='comb2.top100.txt',sep='\t',quote=F)
Error in write(head(comb.de2, 100)$Gene, file = "comb2.top100.txt", sep = "\t", :
unused argument (quote = F)

Look at the intersecting genes
tde <- arrange(tumor.de$tumor,by=desc(Z))
pde <- arrange(prog.de$prog,by=desc(Z))
alpha <- 0.1; size <- 0.2;
ncon$embedding <- ncon$misc$embeddings$umap
n1 <- scon$plotGraph(groups=unfac,palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F)
n1

pl <- lapply(grep("Tumor",names(scon$samples),val=T),function(nam) {
sccore::embeddingPlot(ncon$samples[[nam]]$embeddings$PCA$tSNE,groups=unfac,palette=typefc.pal,plot.na=F,raster=T,raster.height=3,raster.width=3,font.size=c(3,4)) + ggtitle(nam) +theme_bw() + theme(legend.position = "none") + theme(axis.ticks = element_blank(), axis.title = element_blank(), axis.text = element_blank(),panel.grid.major = element_blank(), panel.grid.minor=element_blank())
})
plot_grid(plotlist=pl,nrow=3)

Look at Eli’s genes
qplot <- function(g, con.obj=con, ann=con$clusters$leiden$groups ) {
#cat(g,' ')
x <- lapply(con.obj$samples[!grepl("Whole|Noninv",names(con.obj$samples))],function(r) { if(g %in% colnames(r$counts)) { r$counts[,g] } else { return(NULL) } })
if(length(unlist(x))<1) stop('gene ',g,' is not found')
df <- data.frame(val=unlist(x),cell=unlist(lapply(x,names)))
df$cluster <- ann[match(df$cell,names(ann))]
df <- na.omit(df)
mv <- max(tapply(df$val,df$cluster,quantile,p=0.8),tapply(df$val,df$cluster,mean))*1.5
p <- ggplot(df,aes(x=cluster,y=val,color=cluster))+geom_boxplot(outlier.shape = NA)+ stat_summary(fun.data=mean_se,geom="pointrange", color="black")+ylab(g)+ggtitle(g)+guides(colour=FALSE)+ theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust=0.5))+coord_cartesian(ylim=c(0,mv));
p
}
eli.genes <- c("CXCR4",'GZMB','GZMA','PDCD1','HAVCR2','TIGIT','LAG3','TCF7','ENTPD1','GNLY','CX3CR1','PRF1','SLAMF6')
pl <- lapply(eli.genes,qplot,con.obj=scon,ann=typef)
cowplot::plot_grid(plotlist=pl,ncol=2)
qplot('TGFB2',scon,ann=typef)
Extended differential expression tests
Extend the getPerCellTypeDE function to perform per-sample Wilcoxon test, bootstrap resampled test, as well as a combined Wilcoxon test.
# given a vector of Z scores from resamplign rounds, returns a conservative lower bound of the Z score using a specified quantile (reproducibility power)
conservative.Z <- function(z, quantile) {
bq <- as.numeric(sort(quantile(z,p=c(quantile,1-quantile))))
if(sign(bq[1])!=sign(bq[2])) return(0) # confidence interval includes 0
return(bq[which.min(abs(bq))])
}
# given a vector of p-values from resampling rounds, returns an upper bound based on the desired quantile
conservative.pval <- function(pval, quantile) {
as.numeric(max(quantile(pval,p=c(quantile,1-quantile))))
}
# cm - expression matrix with rows being cells
# cols - a factor on the rows (must match order)
# return: raw and adjusted Z scores
rowWiseWilcoxonTest <- function(cm, cols, lower.lpv.limit=-100) {
# run wilcoxon test comparing each group with the rest
# calculate rank per-column (per-gene) average rank matrix
xr <- pagoda2:::sparse_matrix_column_ranks(cm);
# calculate rank sums per group
grs <- pagoda2:::colSumByFac(xr,as.integer(cols))[-1,,drop=F]
# calculate number of non-zero entries per group
xr@x <- numeric(length(xr@x))+1
gnzz <- pagoda2:::colSumByFac(xr,as.integer(cols))[-1,,drop=F]
#group.size <- as.numeric(tapply(cols,cols,length));
group.size <- as.numeric(tapply(cols,cols,length))[1:nrow(gnzz)]; group.size[is.na(group.size)]<-0; # trailing empty levels are cut off by colSumByFac
# add contribution of zero entries to the grs
gnz <- (group.size-gnzz)
# rank of a 0 entry for each gene
zero.ranks <- (nrow(xr)-diff(xr@p)+1)/2 # number of total zero entries per gene
ustat <- t((t(gnz)*zero.ranks)) + grs - group.size*(group.size+1)/2
# standardize
n1n2 <- group.size*(nrow(cm)-group.size);
# usigma <- sqrt(n1n2*(nrow(cm)+1)/12) # without tie correction
# correcting for 0 ties, of which there are plenty
usigma <- sqrt(n1n2*(nrow(cm)+1)/12)
usigma <- sqrt((nrow(cm) +1 - (gnz^3 - gnz)/(nrow(cm)*(nrow(cm)-1)))*n1n2/12)
z <- t((ustat - n1n2/2)/usigma); # standardized U value- z score
cz <- matrix(qnorm(pagoda2:::bh.adjust(pnorm(as.numeric(abs(z)), lower.tail = FALSE, log.p = TRUE), log = TRUE), lower.tail = FALSE, log.p = TRUE),ncol=ncol(z))*sign(z)
rownames(z) <- rownames(cz) <- colnames(cm); colnames(z) <- colnames(cz) <- levels(cols)[1:ncol(z)];
return(list(z=z,cz=cz))
}
#' Do differential expression for each cell type in a conos object between the specified subsets of apps
#' @param con.obj conos object
#' @param groups factor specifying cell types
#' @param sample.groups a list of two character vector specifying the app groups to compare
#' @param cooks.cutoff cooksCutoff for DESeq2
#' @param independent.filtering independentFiltering for DESeq2
#' @param n.cores number of cores
#' @param cluster.sep.chr character string of length 1 specifying a delimiter to separate cluster and app names
#' @param return.details return detals
#' @export getPerCellTypeDE
getPerCellTypeDE2 <- function(con.obj, groups=NULL, sample.groups=NULL, cooks.cutoff = FALSE, ref.level = NULL, min.cell.count = 10,
independent.filtering = FALSE, n.cores=1, cluster.sep.chr = '<!!>',return.details=TRUE, n.bootstraps=0, bootstrap.quantile=0.9) {
conos:::validatePerCellTypeParams(con.obj, groups, sample.groups, ref.level, cluster.sep.chr)
## Generate a summary dataset collapsing the cells of the same type in each sample
## and merging everything in one matrix
cml <- conos:::rawMatricesWithCommonGenes(con.obj, sample.groups)
aggr2 <- cml %>% lapply(conos:::collapseCellsByType, groups=groups, min.cell.count=min.cell.count) %>% conos:::rbindDEMatrices(cluster.sep.chr=cluster.sep.chr)
# sample groups factor
sample.gfac <- setNames(rep(names(sample.groups),unlist(lapply(sample.groups,length))),unlist(sample.groups))
# cell sample factor
samf <- setNames(rep(names(cml),unlist(lapply(cml,nrow))),unlist(lapply(cml,rownames)))
# cell groups factor
cell.gfac <- setNames(sample.gfac[samf],names(samf))
## For every cell type get differential expression results
de.res <- conos:::papply(conos:::sn(levels(groups)), function(l) {
tryCatch({
## Get count matrix
cm <- aggr2[,strpart(colnames(aggr2),cluster.sep.chr,2,fixed=TRUE) == l]
## Generate metadata
meta <- data.frame(
sample.id= colnames(cm),
group= as.factor(unlist(lapply(colnames(cm), function(y) {
y <- strpart(y,cluster.sep.chr,1,fixed=TRUE)
names(sample.groups)[unlist(lapply(sample.groups,function(x) any(x %in% y)))]
})))
)
if (!ref.level %in% levels(meta$group))
stop('The reference level is absent in this comparison')
meta$group <- relevel(meta$group, ref=ref.level)
if (length(unique(as.character(meta$group))) < 2)
stop('The cluster is not present in both conditions')
dds1 <- DESeq2::DESeqDataSetFromMatrix(cm,meta,design=~group)
dds1 <- DESeq2::DESeq(dds1)
res1 <- DESeq2::results(dds1, cooksCutoff = cooks.cutoff, independentFiltering = independent.filtering)
res1 <- as.data.frame(res1)
# filter cml to leave only the cells of that type and drop the samples with insufficient cells
cmlf <- lapply(cml,function(cm) {
vc <- intersect(rownames(cm), names(groups)[groups==l])
if(length(vc)<min.cell.count) return(NULL)
cm[vc,,drop=F]
})
cmlf <- cmlf[!unlist(lapply(cmlf,is.null))]
# dataset-wise wilcoxon test
x <- rowWiseWilcoxonTest(as(t(cm),'dgCMatrix'), as.factor(meta$group))
res1$wZ <- -x$z[,ref.level]
res1$wZadj <- -x$cz[,ref.level]
#res1$wP <- pmin(pnorm(x$z[,ref.level]),pnorm(x$z[,ref.level],lower.tail=F))
#res1$wPadj <- pmin(pnorm(x$cz[,ref.level]),pnorm(x$cz[,ref.level],lower.tail=F))
# resampled dataset-wise wilcoxon tests
if(n.bootstraps>0) {
xx <- lapply(1:n.bootstraps,function(i) {
cm <- do.call(rbind,lapply(cmlf,function(x) colSums(x[sample(rownames(x),replace = T),])))
rowWiseWilcoxonTest(as(cm,'dgCMatrix'), as.factor(sample.gfac[rownames(cm)]))
})
xx <- do.call(rbind,lapply(xx,function(x) x$z[,ref.level]))
res1$bwZ <- -apply(xx,2,conservative.Z,quantile=bootstrap.quantile)
res1$bwZadj <- qnorm(pagoda2:::bh.adjust(pnorm(as.numeric(abs(res1$bwZ)), lower.tail = FALSE, log.p = TRUE), log = TRUE), lower.tail = FALSE, log.p = TRUE)*sign(res1$bwZ)
}
# cell-wise wilcoxon test
cm2 <- do.call(rbind,cmlf)
x <- rowWiseWilcoxonTest(cm2, as.factor(cell.gfac[rownames(cm2)]))
# res1$cwP <- pmin(pnorm(x$z[,ref.level]),pnorm(x$z[,ref.level],lower.tail=F))
# res1$cwPadj <- pmin(pnorm(x$cz[,ref.level]),pnorm(x$cz[,ref.level],lower.tail=F))
res1$cwZ <- -x$z[,ref.level]
res1$cwZadj <- -x$cz[,ref.level]
res1 <- res1[order(res1$padj,decreasing = FALSE),]
gc()
##
if(return.details) {
list(res=res1, cm=cm, sample.groups=sample.groups)
} else {
res1
}
}, error=function(err) {warning("Error for level ", l, ": ", err$message); return(NA)})
}, n.cores=n.cores)
de.res
}
samplegroups <- list(
Benign = grep("-Whole",names(scon$samples),val=T),
Tumor = grep("-Tumor",names(scon$samples),val=T)
)
de.info2 <- getPerCellTypeDE2(scon, groups=nfac, sample.groups = samplegroups, ref.level='Benign', n.cores=30,n.bootstraps=100)
#saveRDS(de.info2,file='de.TB.rds')
Writing out JSON files with the updated test info:
source("~pkharchenko/m/pavan/DLI/conp2.r")
dir.create('json',showWarnings = F)
# need to get rid of spaces in the names
names(de.info2) <- gsub(" ","_",names(de.info2))
saveDEasJSON2(de.info2,'json/')
write out link table: note that you’ll need to change the location variable to specify where the folder with all the json files and deview.3.html (that has to be copied there manually) will be located on the server:
# path - local path where to save the TOC file
# location - web server path where the json files and the toc file will reside
write.toc.file <- function(de.info,path='json',fname='toc.html',location='http://pklab.med.harvard.edu/peterk/scadden/bmmet/supp/json') {
toc.file <- paste(path,fname,sep='/')
s <- paste(c(list('<html><head><style>
table {
font-family: arial, sans-serif;
border-collapse: collapse;
width: 100%;
}
td, th {
border: 1px solid #dddddd;
text-align: left;
padding: 8px;
}
tr:nth-child(even) {
background-color: #dddddd;
}
</style></head><body><table>'),
suppressWarnings(lapply(names(de.info),function(n) { if(!is.na(de.info[[n]])) { ref <- paste0('<a href="',location,'/deview.3.html?d=',n,'.json">',n,'</a>') } else {ref <- 'NA'}; paste0('<tr><td>',ref,'</td></tr>') })),
list('</table></body></html>')),collapse='\n')
write(s,file=toc.file)
}
write.toc.file(de.info2,path='json',location='http://pklab.med.harvard.edu/peterk/scadden/bmmet/supp/json')
Calculate all contrasts:
dir.create('json.TB',showWarnings = F)
saveDEasJSON2(de.TB,'json.TB/')
dir.create('json.IB',showWarnings = F)
saveDEasJSON2(de.IB,'json.IB/')
write.toc.file(de.TB,path='json.TB',location='http://pklab.med.harvard.edu/peterk/scadden/bmmet/supp/json.TB')
write.toc.file(de.IB,path='json.IB',location='http://pklab.med.harvard.edu/peterk/scadden/bmmet/supp/json.IB')
---
title: "BM revision plots"
output: html_notebook
---

```{r}
library(pagoda2)
library(conos)
library(Matrix)
library(parallel)
library(cowplot)
```



Load original conos object and annotations

```{r}
scon <- Conos$new(readRDS("~pkharchenko/m/scadden/bmmet/jan2019/scon.rds"))
load("~pkharchenko/m/scadden/bmmet/jan2019/annotations.RData")
typefc <- readRDS("/d0-mendel/home/meisl/Workplace/BMME/Figures/data/cell.ano.merged.rds")
typefc.pal <- readRDS("/d0-mendel/home/meisl/Workplace/BMME/Figures/data/cellType.color.rds")
```


Look at the coarse annotation:
```{r fig.width=5, fig.height=5}
alpha <- 0.1; size <- 0.2; 
p1 <- scon$plotGraph(groups=typefc,palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F)
p1
```



Look at the detailed annotation:

```{r fig.width=5, fig.height=5}
alpha <- 0.1; size <- 0.2; 
p2 <- scon$plotGraph(groups=typef,alpha=alpha,size=size,mark.groups=T,plot.na=F)
p2
```


```{r fig.width=5, fig.height=5}
alpha <- 0.1; size <- 0.2; 
scon$plotGraph(gene='MDM4',alpha=0.4,size=size,mark.groups=T,plot.na=F)
```

### Doublets

```{r}
source("~/m/pavan/DLI/conp2.r")
dl <- lapply(scon$samples,function(x) t(x$misc$rawCounts));

ds <- mclapply(dl,get.scrublet.scores,mc.cores=30)
doublet.f <- setNames(unlist(ds),unlist(lapply(ds,names)))
```

```{r fig.width=5, fig.height=5}
alpha <- 0.1; size <- 0.2; 
scon$plotGraph(groups=as.factor(doublet.f[names(doublet.f) %in% names(typef)]>0.25),palette=c("TRUE"='red','FALSE'='gray70'),alpha=alpha,size=size,mark.groups=F,plot.na=F)
```

```{r fig.width=5, fig.height=5}
alpha <- 0.1; size <- 0.2; 
p1 <- scon$plotGraph(groups=typefc[doublet.f[names(typefc)]<=0.25],palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F)
p1
```


### Marker genes

Marker genes
```{r}
nfac <- as.factor(typefc[doublet.f[names(typefc)]<=0.25])
```

```{r}
annot.de <- scon$getDifferentialGenes(groups=nfac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
```


```{r fig.width=12,fig.height=12}
#source("~/m/p2/conos/R/plot.R")
pp <- plotDEheatmap(scon,nfac,annot.de,n.genes.per.cluster = 20 ,show.gene.clusters=T,column.metadata=list(samples=scon$getDatasetPerCell()),order.clusters = T, column.metadata.colors = list(clusters=typefc.pal),use_raster = T,raster_device = "CairoPNG")
pdf(file='annot.heatmap.pdf',width=10,height=30); print(pp); dev.off();
```

A small version of the hetmap, for the main figure
```{r fig.width=6,fig.height=8}
source("~/m/p2/conos/R/plot.R")
genes <- c('MS4A1','CD79A','CD79B','MZB1','VPREB3','SEC11C','IGLL5','GNLY','GZMB','LILRA4','AZU1','MPO','FCN1','IER3','C5AR1','IGJ','SOX4','STMN1','MYL9','HBD','GZMK','AR','KLK2')
pp <- plotDEheatmap(scon,nfac,annot.de,n.genes.per.cluster = 20 ,show.gene.clusters=T, column.metadata.colors = list(clusters=typefc.pal), order.clusters = T, additional.genes = genes, labeled.gene.subset = genes, min.auc = 0.6,use_raster = T,raster_device = "CairoPNG")
pp
```





```{r fig.width=5, fig.height=5}
alpha <- 0.5; size <- 0.1; 
scon$plotGraph(gene='KLK2',alpha=alpha,size=size,mark.groups=F,plot.na=F)
```

### Sample panel:
```{r fig.height=24, fig.width=12}
pp <- scon$plotPanel(groups=nfac,plot.na=F,alpha=0.3,size=0.5,use.common.embedding = F, ncol=4, mark.groups=T,palette=typefc.pal,raster=T, raster.width=3,raster.height=3,title.size=4,font.size=c(3,4))
#pdf('panel.pdf',height=8,width=2); print(pp); dev.off();
pp
```


Just the Tumor fractions
```{r fig.height=10, fig.width=10}
pl <- lapply(grep("Tumor",names(scon$samples),val=T),function(nam) {
  sccore::embeddingPlot(ncon$samples[[nam]]$embeddings$PCA$tSNE,groups=nfac,palette=typefc.pal,plot.na=F,raster=T,raster.height=3,raster.width=3,font.size=c(3,4)) + ggtitle(nam) +theme_bw() + theme(legend.position = "none") + theme(axis.ticks = element_blank(), axis.title = element_blank(), axis.text = element_blank(),panel.grid.major = element_blank(), panel.grid.minor=element_blank())
})
plot_grid(plotlist=pl,nrow=3)
```

```{r fig.height=10, fig.width=10}
pl <- lapply(grep("Tumor",names(scon$samples),val=T),function(nam) {
  sccore::embeddingPlot(ncon$samples[[nam]]$embeddings$PCA$tSNE,colors=conos:::getGeneExpression(ncon$samples[[nam]],'KLK2'),plot.na=F,raster=T,raster.height=3,raster.width=3,font.size=c(3,4)) + ggtitle(nam) +theme_bw() + theme(legend.position = "none") + theme(axis.ticks = element_blank(), axis.title = element_blank(), axis.text = element_blank(),panel.grid.major = element_blank(), panel.grid.minor=element_blank())
})
plot_grid(plotlist=pl,nrow=3)
```

## Expression difference magnitude analysis
An extended version, non-paired comparisons. 

```{r}
source("~pkharchenko/m/p2/comp/cacoa/R/expression_shifts.R")

samplegroups <- list(
  Benign = grep("-Whole",names(scon$samples),val=T),
  Tumor = grep("-Tumor",names(scon$samples),val=T)
)
sgf <- setNames(rep(names(samplegroups),unlist(lapply(samplegroups,length))),unlist(samplegroups))

xTB <- estimateExpressionShiftMagnitudes(lapply(scon$samples,conos:::getRawCountMatrix),sample.groups=sgf,cell.groups=nfac,dist='JS',n.cells=500,n.subsamples=100,min.cells=10,n.cores=30) 
xTBc <- estimateExpressionShiftMagnitudes(lapply(scon$samples,conos:::getRawCountMatrix),sample.groups=sgf,cell.groups=nfac,dist='cor',n.cells=500,n.subsamples=100,min.cells=10,n.cores=30) 


samplegroups <- list(
  Benign = grep("-Whole",names(scon$samples),val=T),
  Tumor = grep("-Involved",names(scon$samples),val=T)
)
sgf <- setNames(rep(names(samplegroups),unlist(lapply(samplegroups,length))),unlist(samplegroups))

xIB <- estimateExpressionShiftMagnitudes(lapply(scon$samples,conos:::getRawCountMatrix),sample.groups=sgf,cell.groups=nfac,dist='JS',n.cells=500,n.subsamples=100,min.cells=10,n.cores=30) 
xIBc <- estimateExpressionShiftMagnitudes(lapply(scon$samples,conos:::getRawCountMatrix),sample.groups=sgf,cell.groups=nfac,dist='cor',n.cells=500,n.subsamples=100,min.cells=10,n.cores=30) 

samplegroups <- list(
  Benign = grep("-Noninvolved",names(scon$samples),val=T),
  Tumor = grep("-Involved",names(scon$samples),val=T)
)
sgf <- setNames(rep(names(samplegroups),unlist(lapply(samplegroups,length))),unlist(samplegroups))

xID <- estimateExpressionShiftMagnitudes(lapply(scon$samples,conos:::getRawCountMatrix),sample.groups=sgf,cell.groups=nfac,dist='JS',n.cells=500,n.subsamples=100,min.cells=10,n.cores=30) 
xIDc <- estimateExpressionShiftMagnitudes(lapply(scon$samples,conos:::getRawCountMatrix),sample.groups=sgf,cell.groups=nfac,dist='cor',n.cells=500,n.subsamples=100,min.cells=10,n.cores=30) 


samplegroups <- list(
  Involved = grep("-Involved",names(scon$samples),val=T),
  Tumor = grep("-Tumor",names(scon$samples),val=T)
)
sgf <- setNames(rep(names(samplegroups),unlist(lapply(samplegroups,length))),unlist(samplegroups))

xIT <- estimateExpressionShiftMagnitudes(lapply(scon$samples,conos:::getRawCountMatrix),sample.groups=sgf,cell.groups=nfac,dist='JS',n.cells=500,n.subsamples=100,min.cells=10,n.cores=30) 
xITc <- estimateExpressionShiftMagnitudes(lapply(scon$samples,conos:::getRawCountMatrix),sample.groups=sgf,cell.groups=nfac,dist='cor',n.cells=500,n.subsamples=100,min.cells=10,n.cores=30) 



samplegroups <- list(
  Distal = grep("-Noninvolved",names(scon$samples),val=T),
  Tumor = grep("-Tumor",names(scon$samples),val=T)
)
sgf <- setNames(rep(names(samplegroups),unlist(lapply(samplegroups,length))),unlist(samplegroups))

xTD <- estimateExpressionShiftMagnitudes(lapply(scon$samples,conos:::getRawCountMatrix),sample.groups=sgf,cell.groups=nfac,dist='JS',n.cells=500,n.subsamples=100,min.cells=10,n.cores=30) 
xTDc <- estimateExpressionShiftMagnitudes(lapply(scon$samples,conos:::getRawCountMatrix),sample.groups=sgf,cell.groups=nfac,dist='cor',n.cells=500,n.subsamples=100,min.cells=10,n.cores=30) 


```

JS-based distances
```{r fig.height=8,fig.width=12}
rlist <- list("Tumor vs. Benign"=xTB,"Involved vs. Benign"=xIB,"Tumor vs. Involved"=xIT,"Tumor vs. Distal"=xTD,"Involved vs. Distal"=xID)
pl <- lapply(names(rlist),function(nam) { 
  ggplot(rlist[[nam]]$df,aes(x=as.factor(Type),y=value)) + 
    geom_boxplot(notch=T,outlier.shape=NA) + 
    geom_jitter(position=position_jitter(0.1), aes(color=patient),show.legend=FALSE,alpha=0.1) + 
    geom_hline(yintercept = 1,linetype=2,color='gray80') +
    ylim(0,min(5,max(rlist[[nam]]$df$value)))+
    theme(axis.text.x=element_text(angle = 90, hjust=1), axis.text.y=element_text(angle=90, hjust=0.5)) +
    ggtitle(nam)+
    labs(x="", y="normalized distance")
})
plot_grid(plotlist=pl,nrow=2)
```
Correlation-based figure
```{r fig.height=8,fig.width=7}
rlist <- list("Tumor vs. Benign"=xTBc,"Involved vs. Benign"=xIBc,"Tumor vs. Distal"=xTDc,"Involved vs. Distal"=xIDc)
pl <- lapply(names(rlist),function(nam) { 
  ggplot(rlist[[nam]]$df,aes(x=as.factor(Type),y=value)) + 
    geom_boxplot(notch=T,outlier.shape=NA) + 
    geom_jitter(position=position_jitter(0.1), aes(color=patient),show.legend=FALSE,alpha=0.1) + 
    geom_hline(yintercept = 1,linetype=2,color='gray80') +
    #ylim(0,min(5,max(rlist[[nam]]$df$value)))+
    ylim(0,5)+
    theme(axis.text.x=element_text(angle = 90, hjust=1), axis.text.y=element_text(angle=90, hjust=0.5)) +
    ggtitle(nam)+
    labs(x="", y="standardized distance")
})
pp <- plot_grid(plotlist=pl,nrow=2)
pdf(file='difference.magnitudes.pdf',width=7,height=8); print(pp); dev.off();
pp
```






## Realignment of the Tumor fraction without translation/mitochondrial genes

```{r}
p2l <- lapply(lapply(dl,function(x) x[,!doublet.f[colnames(x)]>0.25]), basicP2proc, n.cores=30, min.cells.per.gene=0,min.transcripts.per.cell=1e3, n.odgenes=2e3, get.largevis=FALSE, make.geneknn=FALSE)
```

Remove translation and respiratory chain genes that are bringing tumor cells close to progentiors
```{r}
library(biomaRt)
ensembl = useMart("ensembl",dataset="hsapiens_gene_ensembl")
#gene.data <- getBM(attributes=c('hgnc_symbol'),filters = 'go', values = "GO:0000280", mart = ensembl)

#getBM(attributes = c('entrezgene_id','hgnc_symbol'), filters = 'go', values = 'GO:0000278', mart = ensembl)
#getBM(attributes = c('entrezgene_id','hgnc_symbol'), filters = 'go', values = 'GO:0006260', mart = ensembl)
tr.genes <- getBM(attributes = c('entrezgene_id','hgnc_symbol'), filters = 'go', values = c('GO:0006412','GO:0006414','GO:0022904','GO:0055114','GO:0070125','GO:0006839'), mart = ensembl)
tr.genes <- tr.genes$hgnc_symbol
saveRDS(tr.genes,file='tr.genes.rds')
```

```{r}
tr.genes <- readRDS("tr.genes.rds")
p2l <- lapply(lapply(dl,function(x) x[!rownames(x) %in% tr.genes,!doublet.f[colnames(x)]>0.25]), basicP2proc, n.cores=30, min.cells.per.gene=0,min.transcripts.per.cell=1e3, n.odgenes=2e3, get.largevis=FALSE, make.geneknn=FALSE)
```



Conos integration of only tumor fractions:
```{r}
ncon <- Conos$new(p2l[grepl("Tumor",names(p2l))],n.cores=30)
#ncon$buildGraph(k=15, k.self=5, space='CPCA', ncomps=30, n.odgenes=2000, verbose=TRUE)
#ncon$buildGraph(k=15, k.self=5, space='CCA', ncomps=30, n.odgenes=2000, verbose=TRUE)
ncon$buildGraph(k=15, k.self=5, space='PCA', ncomps=30, n.odgenes=2000, verbose=TRUE)
ncon$embedGraph(method='UMAP');
# store different embeddings
ncon$misc$embeddings <- list()
ncon$misc$embeddings$umap <- ncon$embedding
ncon$embedGraph(sgd_batches=2e8,apha=1.5);
ncon$misc$embeddings$lv <- ncon$embedding

ncon$findCommunities(method=leiden.community,resolution=1)
```

Take a look
```{r fig.width=10, fig.height=10}
alpha <- 0.1; size <- 0.5; 
ncon$embedding <- ncon$misc$embeddings$umap
n1 <- ncon$plotGraph(groups=typefc,palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F)
n2 <- ncon$plotGraph(groups=typef,alpha=alpha,size=size,mark.groups=T,plot.na=F,font.size=c(3,5))
#n3 <- ncon$plotGraph(groups=ncon$getDatasetPerCell(),alpha=alpha,size=size,mark.groups=F,plot.na=F)
n3 <- ncon$plotGraph(alpha=alpha,size=size,mark.groups=T,plot.na=F)
plot_grid(plotlist=list(p1,n1,n2,n3),nrow=2)
```


```{r fig.width=3, fig.height=3}
alpha <- 0.1; size <- 0.3; 
n1 <- ncon$plotGraph(groups=typefc,palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F,font.size=c(3,4),raster=T,raster.width=3,raster.height=3)
pdf(file='tumor.nometab.embedding.pdf',width=3,height=3); print(n1); dev.off();
n1
```

```{r fig.width=12, fig.height=6}
alpha <- 0.2; size <- 0.1; 
#n1 <- ncon$plotGraph(groups=typefc,palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F)
n2 <- ncon$plotGraph(groups=typef,alpha=alpha,size=size,mark.groups=T,plot.na=F,font.size=c(3,4))
n3 <- ncon$plotGraph(gene='KLK2',alpha=alpha,size=size,mark.groups=F,plot.na=F)
plot_grid(plotlist=list(n2,n3),nrow=1)
```


Contrast tumor cluster against progenitor cluster 38
```{r}

x <- ncon$clusters[[1]]$groups
tumor.cells <- names(x)[x==42]
prog.cells <- names(x)[x==38]


unfac <- typefc[doublet.f[names(typefc)]<=0.25]
unfac[names(unfac)%in% tumor.cells] <- 'Tumor'
unfac <- as.factor(unfac)



tfac <- ncon$getDatasetPerCell()
tfac <- tfac[!names(tfac) %in% prog.cells]
tfac <- as.factor(setNames(ifelse(names(tfac) %in% tumor.cells,'tumor','other'),names(tfac)))

tumor.de <- ncon$getDifferentialGenes(groups=tfac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)


pfac <- ncon$getDatasetPerCell()
pfac <- pfac[!names(pfac) %in% tumor.cells]
pfac <- as.factor(setNames(ifelse(names(pfac) %in% prog.cells,'prog','other'),names(pfac)))

prog.de <- ncon$getDifferentialGenes(groups=pfac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)


cfac <- ncon$getDatasetPerCell()
cfac <- as.factor(setNames(ifelse(names(cfac) %in% c(prog.cells,tumor.cells),'comb','other'),names(cfac)))

comb.de <- ncon$getDifferentialGenes(groups=cfac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)

# try with the entire progenitor population, balancing sizes
cfac <- ncon$getDatasetPerCell()
x <- names(typefc)[typefc=="Progenitors"];
xs <- sample(x,length(tumor.cells))
pfac <- pfac[!names(pfac) %in% setdiff(x,xs)]
cfac <- as.factor(setNames(ifelse(names(cfac) %in% c(xs,tumor.cells),'comb','other'),names(cfac)))

comb.de2 <- ncon$getDifferentialGenes(groups=cfac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)


```


Top tumor markers
```{r}
require(dplyr)
tde <- arrange(tumor.de$tumor,by=desc(AUC))
tde
```

```{r}
pde <- arrange(prog.de$prog,by=desc(AUC))
pde
```


```{r}
arrange(comb.de$comb,by=desc(AUC))
```

```{r}
arrange(comb.de2$comb,by=desc(AUC))
```

```{r}
write(as.character(head(comb.de2$comb,300)$Gene),file='comb2.top300.txt',sep='\n')
write(unique(unlist(lapply(comb.de2,function(x) as.character(x$Gene)))),file='background.txt',sep='\n')
```


```{r fig.width=5, fig.height=5}
alpha <- 0.1; size <- 0.2; 
scon$plotGraph(gene='BLNK',alpha=0.4,size=size,mark.groups=T,plot.na=F)
```


Look at the intersecting genes
```{r}
tde <- arrange(tumor.de$tumor,by=desc(Z))
pde <- arrange(prog.de$prog,by=desc(Z))
x <- intersect(head(tde,500)$Gene,head(pde,500)$Gene)
write(x,file='intersect.txt',sep='\n')
```




```{r fig.width=8, fig.height=8}
alpha <- 0.1; size <- 0.2; 
ncon$embedding <- ncon$misc$embeddings$umap
n1 <- scon$plotGraph(groups=unfac,palette=typefc.pal,alpha=alpha,size=size,mark.groups=T,plot.na=F)
n1
```

```{r fig.height=10, fig.width=10}
pl <- lapply(grep("Tumor",names(scon$samples),val=T),function(nam) {
  sccore::embeddingPlot(ncon$samples[[nam]]$embeddings$PCA$tSNE,groups=unfac,palette=typefc.pal,plot.na=F,raster=T,raster.height=3,raster.width=3,font.size=c(3,4)) + ggtitle(nam) +theme_bw() + theme(legend.position = "none") + theme(axis.ticks = element_blank(), axis.title = element_blank(), axis.text = element_blank(),panel.grid.major = element_blank(), panel.grid.minor=element_blank())
})
plot_grid(plotlist=pl,nrow=3)
```



## Look at Eli's genes

```{r}
qplot <- function(g, con.obj=con, ann=con$clusters$leiden$groups ) {
  #cat(g,' ')
  x <- lapply(con.obj$samples[!grepl("Whole|Noninv",names(con.obj$samples))],function(r) { if(g %in% colnames(r$counts)) { r$counts[,g] } else { return(NULL) } })
  if(length(unlist(x))<1) stop('gene ',g,' is not found')
  df <- data.frame(val=unlist(x),cell=unlist(lapply(x,names)))
  df$cluster <- ann[match(df$cell,names(ann))]
  df <- na.omit(df)
  
  mv <- max(tapply(df$val,df$cluster,quantile,p=0.8),tapply(df$val,df$cluster,mean))*1.5
  p <- ggplot(df,aes(x=cluster,y=val,color=cluster))+geom_boxplot(outlier.shape = NA)+ stat_summary(fun.data=mean_se,geom="pointrange", color="black")+ylab(g)+ggtitle(g)+guides(colour=FALSE)+ theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust=0.5))+coord_cartesian(ylim=c(0,mv));
  p
}
```


```{r fig.width=10,fig.height=25}
eli.genes <- c("CXCR4",'GZMB','GZMA','PDCD1','HAVCR2','TIGIT','LAG3','TCF7','ENTPD1','GNLY','CX3CR1','PRF1','SLAMF6')
pl <- lapply(eli.genes,qplot,con.obj=scon,ann=typef)
cowplot::plot_grid(plotlist=pl,ncol=2)
```



```{r}
qplot('TGFB2',scon,ann=typef)
```


### Extended differential expression tests

Extend the getPerCellTypeDE function to perform per-sample Wilcoxon test, bootstrap resampled test, as well as a combined Wilcoxon test.

```{r}

# given a vector of Z scores from resamplign rounds, returns a conservative lower bound of the Z score using a specified quantile (reproducibility power)
conservative.Z <- function(z, quantile) {
  bq <- as.numeric(sort(quantile(z,p=c(quantile,1-quantile))))
  if(sign(bq[1])!=sign(bq[2])) return(0) # confidence interval includes 0
  return(bq[which.min(abs(bq))])
}

# given a vector of p-values from resampling rounds, returns an upper bound based on the desired quantile
conservative.pval <- function(pval, quantile) {
  as.numeric(max(quantile(pval,p=c(quantile,1-quantile))))
}

# cm - expression matrix with rows being cells
# cols - a factor on the rows (must match order)
# return: raw and adjusted Z scores
rowWiseWilcoxonTest <- function(cm, cols, lower.lpv.limit=-100) {
  # run wilcoxon test comparing each group with the rest
  # calculate rank per-column (per-gene) average rank matrix
  xr <- pagoda2:::sparse_matrix_column_ranks(cm);
  # calculate rank sums per group
  grs <- pagoda2:::colSumByFac(xr,as.integer(cols))[-1,,drop=F]
  # calculate number of non-zero entries per group
  xr@x <- numeric(length(xr@x))+1
  gnzz <- pagoda2:::colSumByFac(xr,as.integer(cols))[-1,,drop=F]
  #group.size <- as.numeric(tapply(cols,cols,length));
  group.size <- as.numeric(tapply(cols,cols,length))[1:nrow(gnzz)]; group.size[is.na(group.size)]<-0; # trailing empty levels are cut off by colSumByFac
  # add contribution of zero entries to the grs
  gnz <- (group.size-gnzz)
  # rank of a 0 entry for each gene
  zero.ranks <- (nrow(xr)-diff(xr@p)+1)/2 # number of total zero entries per gene
  ustat <- t((t(gnz)*zero.ranks)) + grs - group.size*(group.size+1)/2
  # standardize
  n1n2 <- group.size*(nrow(cm)-group.size);
  # usigma <- sqrt(n1n2*(nrow(cm)+1)/12) # without tie correction
  # correcting for 0 ties, of which there are plenty
  usigma <- sqrt(n1n2*(nrow(cm)+1)/12)
  usigma <- sqrt((nrow(cm) +1 - (gnz^3 - gnz)/(nrow(cm)*(nrow(cm)-1)))*n1n2/12)
  z <- t((ustat - n1n2/2)/usigma); # standardized U value- z score

  cz <- matrix(qnorm(pagoda2:::bh.adjust(pnorm(as.numeric(abs(z)), lower.tail = FALSE, log.p = TRUE), log = TRUE), lower.tail = FALSE, log.p = TRUE),ncol=ncol(z))*sign(z)
  rownames(z) <- rownames(cz) <- colnames(cm); colnames(z) <- colnames(cz) <- levels(cols)[1:ncol(z)];
  return(list(z=z,cz=cz))
}

#' Do differential expression for each cell type in a conos object between the specified subsets of apps
#' @param con.obj conos object
#' @param groups factor specifying cell types
#' @param sample.groups a list of two character vector specifying the app groups to compare
#' @param cooks.cutoff cooksCutoff for DESeq2
#' @param independent.filtering independentFiltering for DESeq2
#' @param n.cores number of cores
#' @param cluster.sep.chr character string of length 1 specifying a delimiter to separate cluster and app names
#' @param return.details return detals
#' @export getPerCellTypeDE
getPerCellTypeDE2 <- function(con.obj, groups=NULL, sample.groups=NULL, cooks.cutoff = FALSE, ref.level = NULL, min.cell.count = 10,
                             independent.filtering = FALSE, n.cores=1, cluster.sep.chr = '<!!>',return.details=TRUE, n.bootstraps=0, bootstrap.quantile=0.9) {
  conos:::validatePerCellTypeParams(con.obj, groups, sample.groups, ref.level, cluster.sep.chr)

  ## Generate a summary dataset collapsing the cells of the same type in each sample
  ## and merging everything in one matrix
  cml <- conos:::rawMatricesWithCommonGenes(con.obj, sample.groups)
  aggr2 <- cml %>% lapply(conos:::collapseCellsByType, groups=groups, min.cell.count=min.cell.count) %>% conos:::rbindDEMatrices(cluster.sep.chr=cluster.sep.chr)
  
  # sample groups factor
  sample.gfac <- setNames(rep(names(sample.groups),unlist(lapply(sample.groups,length))),unlist(sample.groups))
  # cell sample factor
  samf <- setNames(rep(names(cml),unlist(lapply(cml,nrow))),unlist(lapply(cml,rownames)))
  # cell groups factor
  cell.gfac <- setNames(sample.gfac[samf],names(samf))
  
  ## For every cell type get differential expression results
  de.res <- conos:::papply(conos:::sn(levels(groups)), function(l) {
    tryCatch({
      ## Get count matrix
      cm <- aggr2[,strpart(colnames(aggr2),cluster.sep.chr,2,fixed=TRUE) == l]
      ## Generate metadata
      meta <- data.frame(
        sample.id= colnames(cm),
        group= as.factor(unlist(lapply(colnames(cm), function(y) {
          y <- strpart(y,cluster.sep.chr,1,fixed=TRUE)
          names(sample.groups)[unlist(lapply(sample.groups,function(x) any(x %in% y)))]
        })))
      )
      if (!ref.level %in% levels(meta$group))
        stop('The reference level is absent in this comparison')
      meta$group <- relevel(meta$group, ref=ref.level)
      if (length(unique(as.character(meta$group))) < 2)
        stop('The cluster is not present in both conditions')
      dds1 <- DESeq2::DESeqDataSetFromMatrix(cm,meta,design=~group)
      dds1 <- DESeq2::DESeq(dds1)
      res1 <- DESeq2::results(dds1, cooksCutoff = cooks.cutoff, independentFiltering = independent.filtering)
      res1 <- as.data.frame(res1)
      
      
      # filter cml to leave only the cells of that type and drop the samples with insufficient cells
      cmlf <- lapply(cml,function(cm) {
        vc <- intersect(rownames(cm), names(groups)[groups==l])
        if(length(vc)<min.cell.count) return(NULL)
        cm[vc,,drop=F]
      })
      cmlf <- cmlf[!unlist(lapply(cmlf,is.null))]
      
      # dataset-wise wilcoxon test
      x <- rowWiseWilcoxonTest(as(t(cm),'dgCMatrix'), as.factor(meta$group))
      res1$wZ <- -x$z[,ref.level]
      res1$wZadj <- -x$cz[,ref.level]
      #res1$wP <- pmin(pnorm(x$z[,ref.level]),pnorm(x$z[,ref.level],lower.tail=F))
      #res1$wPadj <- pmin(pnorm(x$cz[,ref.level]),pnorm(x$cz[,ref.level],lower.tail=F))
      
      # resampled dataset-wise wilcoxon tests
      if(n.bootstraps>0) {
        xx <- lapply(1:n.bootstraps,function(i) {
          cm <- do.call(rbind,lapply(cmlf,function(x) colSums(x[sample(rownames(x),replace = T),])))
          rowWiseWilcoxonTest(as(cm,'dgCMatrix'), as.factor(sample.gfac[rownames(cm)]))
        })
        xx <- do.call(rbind,lapply(xx,function(x) x$z[,ref.level]))
        
        res1$bwZ <- -apply(xx,2,conservative.Z,quantile=bootstrap.quantile)
        
        res1$bwZadj <- qnorm(pagoda2:::bh.adjust(pnorm(as.numeric(abs(res1$bwZ)), lower.tail = FALSE, log.p = TRUE), log = TRUE), lower.tail = FALSE, log.p = TRUE)*sign(res1$bwZ)
      }
      
      # cell-wise wilcoxon test
      cm2 <- do.call(rbind,cmlf)
      x <- rowWiseWilcoxonTest(cm2, as.factor(cell.gfac[rownames(cm2)]))
      # res1$cwP <- pmin(pnorm(x$z[,ref.level]),pnorm(x$z[,ref.level],lower.tail=F))
      # res1$cwPadj <- pmin(pnorm(x$cz[,ref.level]),pnorm(x$cz[,ref.level],lower.tail=F))
      res1$cwZ <- -x$z[,ref.level]
      res1$cwZadj <- -x$cz[,ref.level]
      
      res1 <- res1[order(res1$padj,decreasing = FALSE),]
          
      gc()
      ##
      if(return.details) {
        list(res=res1, cm=cm, sample.groups=sample.groups)
      } else {
        res1
      }
    }, error=function(err) {warning("Error for level ", l, ": ", err$message); return(NA)})
  }, n.cores=n.cores)
  
  de.res
}
```



```{r}
samplegroups <- list(
  Benign = grep("-Whole",names(scon$samples),val=T),
  Tumor = grep("-Tumor",names(scon$samples),val=T)
)

de.info2  <- getPerCellTypeDE2(scon, groups=nfac, sample.groups = samplegroups, ref.level='Benign', n.cores=30,n.bootstraps=100)
#saveRDS(de.info2,file='de.TB.rds')
```



Writing out JSON files with the updated test info:

```{r}
source("~pkharchenko/m/pavan/DLI/conp2.r")
dir.create('json',showWarnings = F)
# need to get rid of spaces in the names
names(de.info2) <- gsub(" ","_",names(de.info2))

saveDEasJSON2(de.info2,'json/')
```

write out link table: note that you'll need to change the location variable to specify where the folder with all the json files and deview.3.html (that has to be copied there manually) will be located on the server:
```{r}
# path - local path where to save the TOC file
# location - web server path where the json files and the toc file will reside
write.toc.file <- function(de.info,path='json',fname='toc.html',location='http://pklab.med.harvard.edu/peterk/scadden/bmmet/supp/json') {
  toc.file <- paste(path,fname,sep='/')
  s <- paste(c(list('<html><head><style>
table {
  font-family: arial, sans-serif;
  border-collapse: collapse;
  width: 100%;
}

td, th {
  border: 1px solid #dddddd;
  text-align: left;
  padding: 8px;
}

tr:nth-child(even) {
  background-color: #dddddd;
}
</style></head><body><table>'),
suppressWarnings(lapply(names(de.info),function(n) { if(!is.na(de.info[[n]])) { ref <- paste0('<a href="',location,'/deview.3.html?d=',n,'.json">',n,'</a>') } else {ref <- 'NA'}; paste0('<tr><td>',ref,'</td></tr>') })),
list('</table></body></html>')),collapse='\n')
  write(s,file=toc.file)
}
```


```{r}
write.toc.file(de.info2,path='json',location='http://pklab.med.harvard.edu/peterk/scadden/bmmet/supp/json')
```


Calculate all contrasts:
```{r}
samplegroups <- list(
  Benign = grep("-Whole",names(scon$samples),val=T),
  Tumor = grep("-Tumor",names(scon$samples),val=T)
)

de.TB  <- getPerCellTypeDE2(scon, groups=nfac, sample.groups = samplegroups, ref.level='Benign', n.cores=30,n.bootstraps=100)
saveRDS(de.TB,file='de.TB.rds')


samplegroups <- list(
  Benign = grep("-Whole",names(scon$samples),val=T),
  Involved = grep("-Involved",names(scon$samples),val=T)
)
de.IB  <- getPerCellTypeDE2(scon, groups=nfac, sample.groups = samplegroups, ref.level='Benign', n.cores=30,n.bootstraps=100)

samplegroups <- list(
  Distal = grep("-Noninvolved",names(scon$samples),val=T),
  Involved = grep("-Involved",names(scon$samples),val=T)
)
de.ID  <- getPerCellTypeDE2(scon, groups=nfac, sample.groups = samplegroups, ref.level='Distal', n.cores=30,n.bootstraps=100)


samplegroups <- list(
  Involved = grep("-Involved",names(scon$samples),val=T),
  Tumor = grep("-Tumor",names(scon$samples),val=T)
)
de.TI  <- getPerCellTypeDE2(scon, groups=nfac, sample.groups = samplegroups, ref.level='Involved', n.cores=30,n.bootstraps=100)

samplegroups <- list(
  Distal = grep("-Noninvolved",names(scon$samples),val=T),
  Tumor = grep("-Tumor",names(scon$samples),val=T)
)
de.TD  <- getPerCellTypeDE2(scon, groups=nfac, sample.groups = samplegroups, ref.level='Distal', n.cores=30,n.bootstraps=100)

saveRDS(de.TB,file='de.TB.rds')
saveRDS(de.IB,file='de.IB.rds')
saveRDS(de.ID,file='de.ID.rds')
saveRDS(de.TI,file='de.TI.rds')
saveRDS(de.TD,file='de.TD.rds')
```

```{r}
dir.create('json.TB',showWarnings = F)
saveDEasJSON2(de.TB,'json.TB/')
dir.create('json.IB',showWarnings = F)
saveDEasJSON2(de.IB,'json.IB/')
```

```{r}
write.toc.file(de.TB,path='json.TB',location='http://pklab.med.harvard.edu/peterk/scadden/bmmet/supp/json.TB')
write.toc.file(de.IB,path='json.IB',location='http://pklab.med.harvard.edu/peterk/scadden/bmmet/supp/json.IB')
```

